In [1]:
%pylab inline
from gplearn.genetic import SymbolicRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.utils.random import check_random_state
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
import graphviz
In [2]:
# Ground truth
x0 = np.arange(-1, 1, .1)
x1 = np.arange(-1, 1, .1)
x0, x1 = np.meshgrid(x0, x1)
y_truth = x0**2 - x1**2 + x1 - 1
ax = plt.figure().gca(projection='3d')
ax.set_xlim(-1, 1)
ax.set_ylim(-1, 1)
ax.set_xticks(np.arange(-1, 1.01, .5))
ax.set_yticks(np.arange(-1, 1.01, .5))
surf = ax.plot_surface(x0, x1, y_truth, rstride=1, cstride=1, color='green', alpha=0.5)
plt.show()
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rng = check_random_state(0)
# Training samples
X_train = rng.uniform(-1, 1, 100).reshape(50, 2)
y_train = X_train[:, 0]**2 - X_train[:, 1]**2 + X_train[:, 1] - 1
# Testing samples
X_test = rng.uniform(-1, 1, 100).reshape(50, 2)
y_test = X_test[:, 0]**2 - X_test[:, 1]**2 + X_test[:, 1] - 1
In [4]:
est_gp = SymbolicRegressor(population_size=5000,
generations=20, stopping_criteria=0.01,
p_crossover=0.7, p_subtree_mutation=0.1,
p_hoist_mutation=0.05, p_point_mutation=0.1,
max_samples=0.9, verbose=1,
parsimony_coefficient=0.01, random_state=0)
est_gp.fit(X_train, y_train)
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In [5]:
print(est_gp._program)
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est_tree = DecisionTreeRegressor()
est_tree.fit(X_train, y_train)
est_rf = RandomForestRegressor(n_estimators=10)
est_rf.fit(X_train, y_train)
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In [7]:
y_gp = est_gp.predict(np.c_[x0.ravel(), x1.ravel()]).reshape(x0.shape)
score_gp = est_gp.score(X_test, y_test)
y_tree = est_tree.predict(np.c_[x0.ravel(), x1.ravel()]).reshape(x0.shape)
score_tree = est_tree.score(X_test, y_test)
y_rf = est_rf.predict(np.c_[x0.ravel(), x1.ravel()]).reshape(x0.shape)
score_rf = est_rf.score(X_test, y_test)
fig = plt.figure(figsize=(12, 10))
for i, (y, score, title) in enumerate([(y_truth, None, "Ground Truth"),
(y_gp, score_gp, "SymbolicRegressor"),
(y_tree, score_tree, "DecisionTreeRegressor"),
(y_rf, score_rf, "RandomForestRegressor")]):
ax = fig.add_subplot(2, 2, i+1, projection='3d')
ax.set_xlim(-1, 1)
ax.set_ylim(-1, 1)
ax.set_xticks(np.arange(-1, 1.01, .5))
ax.set_yticks(np.arange(-1, 1.01, .5))
surf = ax.plot_surface(x0, x1, y, rstride=1, cstride=1, color='green', alpha=0.5)
points = ax.scatter(X_train[:, 0], X_train[:, 1], y_train)
if score is not None:
score = ax.text(-.7, 1, .2, "$R^2 =\/ %.6f$" % score, 'x', fontsize=14)
plt.title(title)
plt.show()
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dot_data = est_gp._program.export_graphviz()
graph = graphviz.Source(dot_data)
graph.render('images/ex1_child', format='png', cleanup=True)
graph
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print(est_gp._program.parents)
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idx = est_gp._program.parents['donor_idx']
fade_nodes = est_gp._program.parents['donor_nodes']
print(est_gp._programs[-2][idx])
print('Fitness:', est_gp._programs[-2][idx].fitness_)
dot_data = est_gp._programs[-2][idx].export_graphviz(fade_nodes=fade_nodes)
graph = graphviz.Source(dot_data)
graph
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idx = est_gp._program.parents['parent_idx']
fade_nodes = est_gp._program.parents['parent_nodes']
print(est_gp._programs[-2][idx])
print('Fitness:', est_gp._programs[-2][idx].fitness_)
dot_data = est_gp._programs[-2][idx].export_graphviz(fade_nodes=fade_nodes)
graph = graphviz.Source(dot_data)
graph
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from gplearn.genetic import SymbolicTransformer
from sklearn.utils import check_random_state
from sklearn.datasets import load_boston
import numpy as np
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rng = check_random_state(0)
boston = load_boston()
perm = rng.permutation(boston.target.size)
boston.data = boston.data[perm]
boston.target = boston.target[perm]
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from sklearn.linear_model import Ridge
est = Ridge()
est.fit(boston.data[:300, :], boston.target[:300])
print(est.score(boston.data[300:, :], boston.target[300:]))
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function_set = ['add', 'sub', 'mul', 'div', 'sqrt', 'log',
'abs', 'neg', 'inv', 'max', 'min']
gp = SymbolicTransformer(generations=20, population_size=2000,
hall_of_fame=100, n_components=10,
function_set=function_set,
parsimony_coefficient=0.0005,
max_samples=0.9, verbose=1,
random_state=0)
gp.fit(boston.data[:300, :], boston.target[:300])
gp_features = gp.transform(boston.data)
new_boston = np.hstack((boston.data, gp_features))
est = Ridge()
est.fit(new_boston[:300, :], boston.target[:300])
print(est.score(new_boston[300:, :], boston.target[300:]))
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from gplearn.functions import make_function
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def logic(x1, x2, x3, x4):
return np.where(x1 > x2, x3, x4)
logical = make_function(function=logic,
name='logical',
arity=4)
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function_set = ['add', 'sub', 'mul', 'div', logical]
gp = SymbolicTransformer(generations=2, population_size=2000,
hall_of_fame=100, n_components=10,
function_set=function_set,
parsimony_coefficient=0.0005,
max_samples=0.9, verbose=1,
random_state=0)
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gp.fit(boston.data[:300, :], boston.target[:300])
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print(gp._programs[0][906])
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dot_data = gp._programs[0][906].export_graphviz()
graph = graphviz.Source(dot_data)
graph.render('images/ex3_fig1', format='png', cleanup=True)
graph
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from gplearn.genetic import SymbolicClassifier
from matplotlib.colors import ListedColormap
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import make_moons, make_circles, make_classification
from sklearn.neural_network import MLPClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.gaussian_process.kernels import RBF
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from sklearn.metrics import roc_auc_score
from sklearn.datasets import load_breast_cancer
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# Modified from https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html
# Code source: Gaël Varoquaux
# Andreas Müller
# Modified for documentation by Jaques Grobler
# License: BSD 3 clause
h = .02 # step size in the mesh
names = ["Nearest Neighbors", "Linear SVM", "RBF SVM", "Gaussian Process",
"Decision Tree", "Random Forest", "Neural Net", "AdaBoost",
"Naive Bayes", "QDA", "SymbolicClassifier"]
classifiers = [
KNeighborsClassifier(3),
SVC(kernel="linear", C=0.025),
SVC(gamma=2, C=1),
GaussianProcessClassifier(1.0 * RBF(1.0)),
DecisionTreeClassifier(max_depth=5),
RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),
MLPClassifier(alpha=1, tol=0.001),
AdaBoostClassifier(),
GaussianNB(),
QuadraticDiscriminantAnalysis(),
SymbolicClassifier(random_state=0)]
X, y = make_classification(n_features=2, n_redundant=0, n_informative=2,
random_state=1, n_clusters_per_class=1)
rng = np.random.RandomState(2)
X += 2 * rng.uniform(size=X.shape)
linearly_separable = (X, y)
datasets = [make_moons(noise=0.3, random_state=0),
make_circles(noise=0.2, factor=0.5, random_state=1),
linearly_separable
]
figure = plt.figure(figsize=(27, 9))
i = 1
# iterate over datasets
for ds_cnt, ds in enumerate(datasets):
# preprocess dataset, split into training and test part
X, y = ds
X = StandardScaler().fit_transform(X)
X_train, X_test, y_train, y_test = \
train_test_split(X, y, test_size=.4, random_state=42)
x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
np.arange(y_min, y_max, h))
# just plot the dataset first
cm = plt.cm.RdBu
cm_bright = ListedColormap(['#FF0000', '#0000FF'])
ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
if ds_cnt == 0:
ax.set_title("Input data")
# Plot the training points
ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright,
edgecolors='k')
# Plot the testing points
ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6,
edgecolors='k')
ax.set_xlim(xx.min(), xx.max())
ax.set_ylim(yy.min(), yy.max())
ax.set_xticks(())
ax.set_yticks(())
i += 1
# iterate over classifiers
for name, clf in zip(names, classifiers):
ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
clf.fit(X_train, y_train)
score = clf.score(X_test, y_test)
# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, x_max]x[y_min, y_max].
if hasattr(clf, "decision_function"):
Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
else:
Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]
# Put the result into a color plot
Z = Z.reshape(xx.shape)
ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)
# Plot the training points
ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright,
edgecolors='k')
# Plot the testing points
ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright,
edgecolors='k', alpha=0.6)
ax.set_xlim(xx.min(), xx.max())
ax.set_ylim(yy.min(), yy.max())
ax.set_xticks(())
ax.set_yticks(())
if ds_cnt == 0:
ax.set_title(name)
ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'),
size=15, horizontalalignment='right')
i += 1
plt.tight_layout()
plt.show()
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rng = check_random_state(0)
cancer = load_breast_cancer()
perm = rng.permutation(cancer.target.size)
cancer.data = cancer.data[perm]
cancer.target = cancer.target[perm]
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est = SymbolicClassifier(parsimony_coefficient=.01,
feature_names=cancer.feature_names,
random_state=1)
est.fit(cancer.data[:400], cancer.target[:400])
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y_true = cancer.target[400:]
y_score = est.predict_proba(cancer.data[400:])[:,1]
roc_auc_score(y_true, y_score)
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dot_data = est._program.export_graphviz()
graph = graphviz.Source(dot_data)
graph.render('images/ex4_tree', format='png', cleanup=True)
graph
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